#%%
import numpy as np

from sklearn.metrics import dcg_score, ndcg_score 
import numpy as np
from sklearn.utils import check_array
from sklearn.metrics import mean_squared_error
from numpy.linalg import solve
from sklearn.linear_model import Lasso
from sklearn.model_selection import train_test_split

from sklearn.multioutput import MultiOutputRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.decomposition import PCA
from sklearn.preprocessing import MinMaxScaler, StandardScaler
import xgboost as xgb
from numba import njit
from copy import deepcopy

def get_mse(pred, actual):
    # Ignore nonzero terms.
    pred = pred[actual.nonzero()].flatten()
    actual = actual[actual.nonzero()].flatten()
    return mean_squared_error(pred, actual)

def sigmoid(x, a=1):
    return 1/(1+np.exp(-1*a*x))

def sigmoid_derivate(x, a=1):
    return sigmoid(x, a)*(1-sigmoid(x, a))


class ExplicitMFRankF():
    def __init__(self, 
                 ratings,
                 valid_ratings,
                 n_factors=40,
                 learning='sgd',
                 item_fact_reg=0.0, 
                 user_fact_reg=0.0,
                 item_bias_reg=0.0,
                 user_bias_reg=0.0,
                 verbose=False):
        """
        Train a matrix factorization model to predict empty 
        entries in a matrix. The terminology assumes a 
        ratings matrix which is ~ user x item
        
        Params
        ======
        ratings : (ndarray)
            User x Item matrix with corresponding ratings
        
        n_factors : (int)
            Number of latent factors to use in matrix 
            factorization model
        learning : (str)
            Method of optimization. Options include 
            'sgd' or 'als'.
        
        item_fact_reg : (float)
            Regularization term for item latent factors
        
        user_fact_reg : (float)
            Regularization term for user latent factors
            
        item_bias_reg : (float)
            Regularization term for item biases
        
        user_bias_reg : (float)
            Regularization term for user biases
        
        verbose : (bool)
            Whether or not to printout training progress
        """
        
        self.ratings = ratings
        self.valid_ratings = valid_ratings
        self.n_users, self.n_items = ratings.shape
        self.n_factors = n_factors
        self.item_fact_reg = item_fact_reg
        self.user_fact_reg = user_fact_reg
        self.item_bias_reg = item_bias_reg
        self.user_bias_reg = user_bias_reg
        self.learning = learning
        if self.learning == 'sgd':
            # self.sample_row, self.sample_col = self.ratings.nonzero()
            # self.n_samples = len(self.sample_row)
            self.n_samples, self.n_configs = self.ratings.shape[0], self.ratings.shape[1]
        self._v = verbose
        self.train_loss_ = [0]
        self.valid_loss_ = [0]
        self.learning_rates_ = []
 

    def get_train_dcg(self, user_vecs, item_vecs):
        #make sure it is non zero            
        user_vecs[np.isnan(self.user_vecs)] = 0
            
        ndcg_s = []
        for w in range(self.ratings.shape[0]):
            ndcg_s.append(ndcg_score([self.ratings[w,:]], [np.dot(user_vecs[w,:], item_vecs.T)]))
            
        return np.mean(ndcg_s)
    
    def train(self, meta_features, valid_meta=None, n_iter=10, learning_rate=0.1, n_estimators=100, max_depth=10, max_rate=1.05, min_rate=0.1, discount=0.95, n_steps=10):
        """ Train model for n_iter iterations from scratch."""
        
        self.pca = PCA(n_components=self.n_factors)
        self.pca.fit(meta_features)
        
        meta_features_pca = self.pca.transform(meta_features)
        meta_valid_pca = self.pca.transform(valid_meta)
        
        
        self.scalar = StandardScaler()
        self.scalar.fit(meta_features_pca)
        
        meta_features_scaled = self.scalar.transform(meta_features_pca)
        meta_valid_scaled = self.scalar.transform(meta_valid_pca)

        self.user_vecs = meta_features_scaled
        
        self.item_vecs = np.random.normal(scale=1./self.n_factors,
                                          size=(self.n_items, self.n_factors))
        
        step_size = (max_rate-min_rate)/(n_steps-1)
        lr_list = list(np.arange(min_rate, max_rate, step_size))
        lr_list.append(max_rate)
        lr_list_reverse = deepcopy(lr_list)
        lr_list_reverse.reverse()
        
        learning_rate_full = []
        for w in range(n_iter):
            learning_rate_full.extend(lr_list)
            learning_rate_full.extend(lr_list_reverse)
            
        self.learning_rate_ = min_rate
        self.learning_rates_.append(self.learning_rate_)

        ctr = 1
        np_ctr = 1
        while ctr <= n_iter:

            self.learning_rate_ = learning_rate_full[ctr-1]
            self.learning_rates_.append(self.learning_rate_)
            # if ctr % 10 == 0 and self._v:
            # print ('\tcurrent iteration: {}'.format(ctr))
            # print('ALORS Rank Fixed iteration', ctr, ndcg_score(self.ratings, np.dot(self.user_vecs, self.item_vecs.T)))
            # self.learning_rates_.append(self.learning_rate)
            ndcg_s = []
            for w in range(self.ratings.shape[0]):
                ndcg_s.append(ndcg_score([self.ratings[w,:]], [np.dot(self.user_vecs[w,:], self.item_vecs.T)], k=self.n_items))
            
            # print('ALORS Fixed iteration', ctr, ndcg_score(self.ratings, np.dot(self.user_vecs, self.item_vecs.T)))
            # print('ALORS Rank Fixed iteration', ctr, 'training', np.mean(ndcg_s))
            self.train_loss_.append(np.mean(ndcg_s))

            ndcg_s = []
            for w in range(self.valid_ratings.shape[0]):
                ndcg_s.append(ndcg_score([self.valid_ratings[w,:]], [np.dot(meta_valid_scaled[w,:], self.item_vecs.T)], k=self.n_items))
            
            # print('ALORS Fixed iteration', ctr, ndcg_score(self.ratings, np.dot(self.user_vecs, self.item_vecs.T)))
            # print('ALORS Rank Fixed iteration', ctr, 'valid', np.mean(ndcg_s))
            self.valid_loss_.append(np.mean(ndcg_s))


            print('ALORS Rank Fixed Sigmoid iteration', ctr, 'train', self.train_loss_[-1], 'valid', self.valid_loss_[-1], 
                  'learning rate', self.learning_rates_[-1])
 
            # improvement is smaller than 1 perc
            if ((self.valid_loss_[-1] - self.valid_loss_[-2])/self.valid_loss_[-2]) <= 0.001:
                # print(((self.valid_loss_[-1] - self.valid_loss_[-2])/self.valid_loss_[-2]))
                np_ctr += 1
            else:
                np_ctr = 1 
            if np_ctr > 3:
                break
            
            # update learning rates
            # self.learning_rate_ = self.learning_rate_ + 0.05
            # self.learning_rates_.append(self.learning_rate_)
            # if ctr % 2:
            #     if ctr <=50:
            #         self.learning_rate_ = min_rate * np.power(discount,ctr)
            #     else:
            #         self.learning_rate_ = min_rate * np.power(discount,50)
                
            # else:
            #     if ctr <=50:
            #         self.learning_rate_ = max_rate * np.power(discount,ctr)
            #     else:
            #         self.learning_rate_ = max_rate * np.power(discount,50)
                    
            # self.learning_rates_.append(self.learning_rate_)
            
            train_indices = list(range(self.n_samples))
            np.random.shuffle(train_indices)
            # print(train_indices)
            
            for h in train_indices:
                
                uh = self.user_vecs[h, :].reshape(1, -1)
                # print(uh.shape)
                grads = []
                
                for i in range(self.n_configs):
                # outler loop     
                    vi = self.item_vecs[i,:].reshape(-1, 1)
                    phis = []
                    rights = []
                    rights_v = []
                    # remove i from js 
                    js = list(range(self.n_configs))
                    js.remove(i)
                    
                    for j in js:
                        vj = self.item_vecs[j,:].reshape(-1, 1)
                        # temp_vt = np.exp(np.matmul(uh, (vj-vi)))
                        # temp_vt = np.ndarray.item(temp_vt)
                        temp_vt = sigmoid(np.ndarray.item(np.matmul(uh, (vj-vi))), a=1)
                        temp_vt_derivative = sigmoid_derivate(np.ndarray.item(np.matmul(uh, (vj-vi))), a=1)
                        # print(uh.re, (self.item_vecs[j,:]-self.item_vecs[i,:]).T.shape)
                        # print((self.item_vecs[j,:]-self.item_vecs[i,:]).reshape(-1, 1).shape)
                        # print(temp_vt.shape)
                        # assert (len(temp_vt)==1)
                        phis.append(temp_vt)
                        rights.append(temp_vt_derivative*(vj-vi))
                        rights_v.append(temp_vt_derivative*uh)
                    phi = np.sum(phis)+1.5
                    rights = np.asarray(rights).reshape(self.n_configs-1, self.n_factors)
                    rights_v = np.asarray(rights_v).reshape(self.n_configs-1, self.n_factors)
                    
                    # print(rights.shape, rights_v.shape)

                    right = np.sum(np.asarray(rights), axis=0)
                    right_v = np.sum(np.asarray(rights_v), axis=0)
                    # print(right, right_v)

                    # print(np.asarray(rights).shape, np.asarray(right).shape)
                    grad = (10**(self.ratings[h, i])-1) / (phi * (np.log(phi))**2)*right
                    grad_v = (10**(self.ratings[h, i])-1) / (phi * (np.log(phi))**2)*right_v
                    
                    self.item_vecs[i, :] +=  self.learning_rate_ * grad_v
                    
                    # print(h, i, grad.shape)
                    grads.append(grad)
                
                grads_uh = np.asarray(grads)
                grad_uh = np.sum(grads_uh, axis=0)
                
                self.user_vecs[h, :] -=  self.learning_rate_ * grad_uh
                # print(self.learning_rate_)
            
            ctr += 1
        
        # self.regr_multirf = MultiOutputRegressor(RandomForestRegressor(
        #     n_estimators=n_estimators, max_depth=max_depth, n_jobs=4))

        # self.regr_multirf = MultiOutputRegressor(Lasso()))
        # self.regr_multirf = MultiOutputRegressor(xgb.XGBRegressor(n_estimators=n_estimators))
        
        # self.regr_multirf.fit(meta_features, self.user_vecs)
        return self



    def predict(self, u, i):
        """ Single user and item prediction."""
        # prediction = self.global_bias + self.user_bias[u] + self.item_bias[i]
        prediction = self.user_vecs[u, :].dot(self.item_vecs[i, :].T)        
        # prediction += self.user_vecs[u, :].dot(self.item_vecs[i, :].T)
        return prediction
    
    # def predict_all(self):
    #     """ Predict ratings for every user and item."""
    #     predictions = np.zeros((self.user_vecs.shape[0], 
    #                             self.item_vecs.shape[0]))
    #     for u in range(self.user_vecs.shape[0]):
    #         for i in range(self.item_vecs.shape[0]):
    #             predictions[u, i] = self.predict(u, i)
                
    #     return predictions
        
    def predict_new(self, test_meta):
        test_meta = check_array(test_meta)
        # assert (test_meta.shape[1]==200)
        
        test_meta_scaled = self.pca.transform(test_meta)
        # print('B', test_meta_scaled.shape)
        
        test_meta_scaled = self.scalar.transform(test_meta_scaled)
        
        # predicted_scores = np.dot(test_k, self.item_vecs.T) + self.item_bias
        predicted_scores = np.dot(test_meta_scaled, self.item_vecs.T)        
        #print(predicted_scores.shape)
        assert (predicted_scores.shape[0]== test_meta.shape[0])
        assert (predicted_scores.shape[1]==self.ratings.shape[1])
        
        return predicted_scores
    
#####################################
# random_state = np.random.RandomState(42)

# r = list(range(100))
# X = random_state.choice(r, size=[100, 5], replace=True)/100
# X_meta = random_state.choice(r, size=[100, 8], replace=True)

# X_train, X_test, X_train_meta, X_test_meta = train_test_split(X, X_meta, test_size=0.33, random_state=42)

# train_data_cv, valid_data_cv, train_roc_cv, valid_roc_cv = train_test_split(X_train_meta, X_train, test_size=0.2)


# EMF = ExplicitMFRankF(train_roc_cv, valid_roc_cv, n_factors=3, learning='sgd', verbose=False)
# EMF.train(n_iter=200, meta_features=train_data_cv, valid_meta=valid_data_cv, learning_rate=0.05, min_rate=0.05, max_rate=0.2, discount=0.98)

# U = EMF.user_vecs
# V = EMF.item_vecs

# pred_scores = np.dot(U, V.T)

# print('rating matrix size:', train_roc_cv.shape)
# print('Our modified loss and gradient results in NDCG:', ndcg_score(train_roc_cv, pred_scores))
# print()

# for j in range(10):
#     U = np.random.normal(size=U.shape)
#     V = np.random.normal(size=V.shape)
#     pred_scores = np.dot(U, V.T)

#     print('trial', j, 'random U, V result in NDCG:', ndcg_score(train_roc_cv, pred_scores))

# # bias_global = EMF.global_bias
# # bias_user = EMF.user_bias
# # bias_item = EMF.item_bias

# # # print(EMF.regr_multirf.predict(test_meta).shape)
# predicted_scores = EMF.predict_new(X_test_meta)
# # predicted_scores_max = np.nanargmax(predicted_scores, axis=1)
